Search Results (3,606)

Background/Objectives: Exercise intolerance is clinically important but conceptually unstable in cardiovascular care. Its relationship with exercise tolerance, exercise capacity, functional capacity and the nursing concept of activity intolerance remains unclear, and the term entered nursing from other disciplines without corresponding conceptual scrutiny. This protocol will examine how these concepts are used in adults with cardiovascular disease, how use varies across conditions, care settings, pathway phases and time, and which attributes, antecedents, consequences and related concepts are reported, to determine what these concepts can mean for nursing. Methods: The study has two interconnected phases: a scoping review following Joanna Briggs Institute methodology and PRISMA-ScR and PRISMA-S reporting, followed by Rodgers’ evolutionary concept analysis. Searches will cover MEDLINE, CINAHL, Embase, Web of Science and Scopus, complemented by gray literature and standardized nursing terminologies. Expected Results: The review will map how exercise intolerance, activity intolerance and neighboring terms are used and will chart which defining attributes and empirical referents are recognizable through routine nursing assessment, require instrumented testing or both. Whether a stable nursing formulation can be derived is treated as an open question: convergence, divergence, instability and insufficient evidence are all possible, and attributes recognizable only through instrumented testing will be reported as a null result for nursing operationalization. Conclusions: The review will determine how the concept has evolved for the discipline of nursing and whether nursing assessment can operationalize it. It will not establish professional consensus, diagnostic thresholds, measurement validity or device specifications. Registration: This study was registered in the Open Science Framework (OSF) Registries before screening and data charting commenced. Full article

Coordinating multi-agent unmanned aerial vehicle (UAV) swarms is challenging in disaster scenarios where communications are dynamic, unreliable, and subject to UAV losses. While distributed artificial intelligence has enabled unprecedented levels of autonomous multi-agent coordination, most methods implicitly take communication topology as a given, not accounting for its limited maintenance in such scenarios. As a result, communication fragmentation can undermine autonomous mission progress during highly dynamic communications conditions. This paper proposes the Adaptive k-Truss-Constrained Agentic AI Framework (ATAC), an AI-based decentralized coordination framework for multi-agent UAV systems, which explicitly factors in graph-theoretic structural considerations during decision-making. The swarm is modeled as a graph, where an adaptive k-truss backbone is maintained during dynamic communication conditions to preserve triangle-based redundancy. Each agent acts as a graph-aware AI entity which bases its decentralized decisions on local information and descriptors of the communication backbone. A closed-loop evolutionary process is used to rebuild the backbone after significant communication link losses while UAVs make mission progress decisions based on information from the current backbone, enabling continuous adaption of the swarm structure to the communication state. The efficacy of the proposed framework is demonstrated through a comprehensive simulation campaign which includes communication link losses, UAV failures, adaptive truss selection, ablation studies, reward sensitivity analysis, and computational performance assessments. ATAC is compared to alternative graph-aware coordination approaches, showing consistent improvements in maintaining communication, preserving backbone structure, enabling triangle-based connectivity, and overall structural recovery while still achieving high-levels of mission progress during dynamic disaster response scenarios. The results highlight the effectiveness of explicitly tying AI-driven decentralized decision-making to maintenance of a graph-theoretic backbone structure for resilient UAV swarm coordination. Full article

Graph convolutional networks (GCNs) have become a mainstream approach for skeleton-based action recognition by modeling spatial dependencies among human joints. However, existing methods mostly rely on learnable adjacency matrices or average relations over the entire action sequence to construct graph structures. Such designs mainly represent the overall static structure of the skeleton and may be insufficient for characterizing dynamic changes in joint relations during action execution. To address this problem, this paper proposes a graph convolutional network with joint relation topology evolution modeling, termed JRTE-GCN. Specifically, the proposed method first constructs a topology evolution matrix from frame-level joint distance sequences by calculating the temporal standard deviation and the difference between the initial and final stages of an action. This matrix reflects both the fluctuation intensity and stage-wise variation of joint relations. Subsequently, persistent homology is used to extract structural features from the matrix, thereby capturing the dynamic evolution of joint relations. Finally, the extracted evolutionary features are transformed into layer-wise biases and fused with the average topological bias through a learnable residual mechanism. The fused biases are incorporated into the graph convolutional feature extraction process, enabling joint modeling of the overall skeleton structure and dynamic relational changes. Experimental results on NTU RGB+D 60, NTU RGB+D 120, and Northwestern-UCLA show that JRTE-GCN achieves competitive recognition accuracy under multiple evaluation protocols, demonstrating the effectiveness of the proposed method. Full article

Against the backdrop of high-quality development in rural tourism, issues such as landscape homogenization and insufficient coordination among diverse stakeholders have become increasingly prominent, creating a marked disparity with the requirements for distinctive and differentiated development in rural revitalization. This paper aims to investigate how actor-networks drive the dynamic evolution of rural tourism landscapes through translation mechanisms, and to illuminate the agentic role of non-human elements in this process, thereby addressing the core question of why the same set of local resources generates distinct landscape forms across different developmental stages. To this end, this study takes the Mayangxi Ecotourism Area in Fujian as a case study and, drawing on actor-network theory, employs qualitative research methods including in-depth interviews, participant observation, and grounded theory to systematically analyze the phased evolutionary characteristics, actor-network translation logic, and underlying operational mechanisms of rural tourism landscape creation. The findings reveal that: (1) the creation of the Mayangxi rural tourism landscape has successively undergone three major developmental stages—landscape construction, landscape integration, and landscape optimization—during which human and non-human actors, through a complete translation mechanism, drive the continuous iteration of the actor-network, with non-human elements playing a significant agentic role throughout the landscape’s evolution; (2) the local landscape creation has formed a closed-loop operational mechanism of “base activation → network construction → network iteration → sustainable operation,” which effectively supports the sustainable development of rural tourism landscapes. This study addresses the analytical limitations of existing rural landscape research, which tends to focus on human-centered interactions while overlooking the value of non-human elements, expands the application boundaries of actor-network theory in the field of rural tourism, and provides practical guidance for differentiated landscape creation in China’s rural tourism development. Full article

Heating, ventilation and air conditioning (HVAC) accounts for an overwhelmingly large proportion of building energy consumption. Mass low-grade cold and heat energy dissipates during system operation, endowing pulsating heat pipes (PHPs) with promising application prospects in building energy systems. Combining experimental tests and the phase-space reconstruction method, this paper investigates the intrinsic correlation between the flow behavior of working fluids and chaotic characteristics under varied working fluids, heating powers and liquid filling ratios. The working fluid type dominates the oscillation characteristics of pulsating heat pipes. When distilled water serves as the working fluid, the attractor presents a scattered distribution. In contrast, the PHP charged with HFE-7100 achieves stable unidirectional circulation with high-frequency, small-amplitude pulsation, forming a densely distributed attractor. Increasing the flow velocity of the working fluid drives the attractor distribution to evolve from scattered to concentrated. Intermittent flow of the working fluid induces a multi-temperature-zone distribution on the tube wall, and the attractor takes on a multi-region spiral morphology. A low liquid filling ratio triggers working fluid dry-out, and the attractor trajectory maintains a continuous unidirectional upward trend; by comparison, the attractor shows a multi-region spiral distribution under high filling ratio conditions. Research on chaotic dynamic characteristic identification, evolutionary law analysis and stable domain regulation of pulsating heat pipes can lay a theoretical foundation for structural optimization and operating condition adjustment of high-performance pulsating heat pipe devices for building waste heat recovery. Full article

Background: Carbapenem-resistant Acinetobacter baumannii (CRAB) is a major nosocomial pathogen; bacteriophage therapy is a promising alternative, but its principal challenge is the rapid emergence of phage resistance. Cocktails counter this yet they are usually assembled by simply pooling phages active against the bacterium, not designed against the resistant mutants that arise. Methods: Next evolutionary phage typing (NEPT) addresses this by inducing resistance to a primary phage and then selecting secondary phages that lyse the resulting resistant mutant. Because choosing effective secondary phages still relies on qualitative visual reading of plaques (size and clarity), this study aimed to identify a rapid quantitative criterion. Results: Using the clinical strain CRAB 43895 and primary phage ϕ8, we obtained 65 NEPT-derived secondary phages that lyse the ϕ8-resistant mutant (ϕ8R) and combined four quantitative indicators—relative bacterial growth, lytic capability, coverage rate, and plaque morphology—with binary logistic regression to predict effective inhibition by the ϕ8 + secondary-phage cocktail (96-h OD₆₀₀ < 0.1). Of the four, only lytic capability (the titer after 3 h of co-culture) independently predicted effective inhibition (ROC AUC = 0.76; at ≥10⁷ plaque-forming units (PFU)/mL, sensitivity 0.83, specificity 0.69). Conclusions: Lytic capability thus provides a rapid (~8–12 h), quantitative criterion for selecting effective secondary phages within the NEPT framework, improving on conventional qualitative typing. Full article

Background: Multiple myeloma (MM) remains an incurable plasma cell malignancy characterized by marked clinical heterogeneity. Existing prognostic frameworks, including the International Staging System (ISS) and FISH-defined cytogenetic risk, are anchored at diagnosis and do not capture the evolutionary dynamics of disease or treatment response, leaving an unmet need for risk models that retain prognostic validity longitudinally. Methods: Using transcriptomic data from 762 CD138-selected MM plasma cells from newly diagnosed patient samples in the MMRF CoMMpass study (NCT01454297), we computed single-sample pathway activity scores for 469 curated cancer-relevant pathways (MSigDB Hallmark; Reactome) and learned a Bayesian causal network linking pathway activity to survival. The model was validated in five independent diagnostic cohorts (n = 1255) and in two independent treatment and relapsed/refractory cohorts (n = 319). Longitudinal risk tracking was additionally assessed in a 46-patient subset of the discovery cohort with serial pre- and post-treatment sampling. Results: The network identified five pathways associated with survival: unfolded protein response (UPR), FLT3 signaling through SRC family kinases, G2M DNA replication checkpoint, metabolism of selenium compound (SeMet), and nicotinate metabolism. The composite survival score stratified patients into high-risk (n = 76; 10%) and standard-risk groups with markedly divergent survival (median 1170 days vs. not reached; p < 0.0001). The score remained an independent prognostic factor after adjustment for age, sex, ISS stage, and KRAS, TP53, and UBR5 mutational status (HR 4.93; 95% CI 2.96–8.19; p < 0.001), and replicated across all five external diagnostic cohorts. Critically, the model retained prognostic discrimination in previously treated (GSE57317; p < 0.0001) and relapsed/refractory (GSE9782; p < 0.0001) settings, and patients transitioning from standard- to high-risk between serial samples exhibited significantly inferior survival compared to standard-risk patients. Conclusions: This pathway-based Bayesian network provides a reproducible, dynamically applicable risk model for MM that captures information complementary to ISS and FISH-defined cytogenetics. The framework supports longitudinal patient monitoring and may inform trial enrichment strategies and closer surveillance for high-risk subpopulations. Full article

Fatty acid desaturase (FAD) is a core catalytic enzyme in plants for the synthesis of unsaturated fatty acids, profoundly affecting plant growth, development, and adaptability to various environmental stresses. The walnut (Juglans regia L.) is an important woody oil tree species, and its kernel is rich in unsaturated fatty acids. Systematic identification of the walnut FAD gene family and analysis of its function are of great significance for revealing the molecular mechanisms underlying unsaturated fatty acid metabolism in the walnut. Based on walnut whole-genome data, this study used homology alignment and hidden Markov model search methods to identify the JrFAD gene family members. Subsequently, a variety of bioinformatics tools were used to systematically analyze their structural characteristics, evolutionary expansion mechanism, expression regulation, and function. A total of 21 JrFAD gene family members were identified and classified into five subfamilies. The family genes were unevenly distributed on nine chromosomes. WGD/segmental duplication was the main expansion method, and the duplicated gene pairs experienced strong purification selection. The family gene promoter sequence is rich in regulatory elements that respond to light, plant hormones, and various stresses. The expression pattern analysis showed that JrFAD3.1 and JrFAD2.3 showed high expression specifically during the rapid accumulation of walnut kernel oil. This study clarified the composition and evolutionary characteristics of the FAD gene family in the walnut, which provides useful information for in-depth analyses of its functional mechanism in the regulation of lipid metabolism, and also identified potential candidate gene resources for the genetic improvement of walnut varieties with high amounts of unsaturated fatty acids. Full article

Bio-inspired optimization algorithms have become an effective class of techniques for addressing challenging continuous optimization problems. In this work, we introduce the Parallel Enzyme Action Optimization (PEAO) algorithm, a parallel bio-inspired optimization approach that incorporates a multi-strategy communication mechanism among cooperative subpopulations. The population is partitioned into multiple subpopulations that evolve concurrently, promoting a more effective exploration of the search space. Furthermore, PEAO integrates adaptive search factors, local search procedure and communication strategies to improve solution quality while reducing the risk of premature convergence. In addition, a K-means-based population initialization procedure and a convergence-driven stopping criterion based on successive improvements in the best objective-function value are incorporated to reduce unnecessary objective-function evaluations and improve the overall efficiency of the optimization process. Full article

This paper develops a coupled behavioral–epidemiological framework for infectious-disease dynamics by integrating an SIQRS compartmental model with an evolutionary game-theoretic representation of quarantine behavior. Unlike conventional epidemic models with exogenously specified behavioral rates, the proposed framework allows transmission and quarantine uptake to respond endogenously to payoff differences between behavioral strategies. This creates a feedback mechanism in which epidemic conditions influence individual incentives, while behavioral adaptation subsequently modifies disease transmission. The mathematical analysis establishes well-posedness and positive invariance of the coupled system, characterizes the disease-free equilibrium and the associated reproduction threshold, and investigates local stability. Sufficient small-gain conditions are also derived to characterize stability of the coupled behavioral–epidemiological dynamics. A discrete-time formulation based on the forward-Euler method is developed together with positivity and consistency conditions. Numerical experiments compare easy, moderate, and strict behavioral-response regimes under a common epidemiological and payoff parameterization. The results show that stronger behavioral responsiveness reduces the principal infectious peak and delays its occurrence. However, behavioral mitigation does not necessarily imply disease eradication, as waning immunity can replenish the susceptible population and generate recurrent epidemic activity over longer horizons. The discrete-time simulations closely reproduce the continuous-time trajectories for a sufficiently small time step, supporting the consistency of the numerical formulation. Overall, the proposed framework provides a mathematically tractable approach for studying the coevolution of strategic behavior and epidemic transmission and for evaluating how behavioral incentives can modify the magnitude, timing, and recurrence of epidemic outbreaks. Full article

The Chinese longsnout catfish is a typical carnivorous fish with a relatively weak ability to utilize glucose. However, the genomic basis for its glucose metabolic adaptation remains unclear. In this study, we used comparative genomics methods to systematically analyze the evolutionary characteristics of glucose metabolism-related genes in the Chinese longsnout catfish, focusing on gene family evolution, patterns of expansion and contraction, and selective pressures. The results indicate that glucose metabolism-related genes have undergone significant reshaping during evolution. Genes involved in glucose digestion, absorption, and insulin signaling pathways demonstrate a tendency toward contraction, while those associated with protein and lipid metabolism exhibit expansion. This pattern is consistent with the species’ long-term adaptation to a high-protein, high-fat diet. Comparative analysis further revealed that, compared to fish with different dietary habits, certain key genes involved in glycolysis in the Chinese longsnout catfish exhibit a reduction in copy number. Molecular evolutionary analysis showed that key genes involved in glycolysis and gluconeogenesis (including hexokinase 2 (hk2), phosphofructokinase, muscle/platelet (pfkm/p)) exhibit signs of accelerated evolution or positive selection. Notably, the PFK gene family exhibits complex evolutionary characteristics resulting from the combined effects of gene contraction, rapid evolution, and positive selection. In summary, this study reveals the genomic evolutionary basis for the glucose metabolic adaptation of the Chinese longsnout catfish and identifies the PFK gene family as a key candidate for elucidating its unique glucose metabolic characteristics. Full article

Multi-objective optimization has become an indispensable tool for solving engineering design and manufacturing problems involving multiple conflicting objectives. This paper presents a novel parameter-less multi-objective optimization (MOO) framework that combines the strengths of evolutionary MOO techniques with the parameter-free search philosophy of the Jaya and Rao algorithms. The proposed framework incorporates non-dominated sorting, elite archiving, and crowding-distance mechanisms to achieve an effective balance between convergence and diversity while eliminating the need for algorithm-specific control parameters. The proposed framework is first validated on sixteen widely used unconstrained benchmark problems comprising five ZDT, seven DTLZ, two IDTLZ, and two SDTLZ test suites using the maximum number of function evaluations reported in the literature. Its performance is evaluated using five widely accepted quality indicators, namely Generational Distance (GD), Inverted Generational Distance (IGD), Hypervolume (HV), Spacing (SP), and Spread (SD). The benchmark results demonstrate that the proposed framework produces competitive Pareto-optimal fronts and exhibits excellent convergence, diversity, and solution distribution compared with several state-of-the-art evolutionary multi-objective optimization algorithms. The practical applicability of the proposed framework is demonstrated through five representative manufacturing optimization problems involving Selective Laser Melting, Microwave Hybrid Heating, Sustainable Machining, Wire Electrical Discharge Machining, and Wire Arc Additive Manufacturing. These case studies encompass additive, thermal, subtractive, and many-objective manufacturing optimization problems with conflicting performance measures. The generated Pareto-optimal solutions are subsequently ranked using the recently developed BHARAT (Best Holistic Adaptable Ranking of Attributes Technique) multi-attribute decision-making method to identify the most suitable compromise solutions. The obtained results demonstrate that the proposed parameter-less MOO framework provides a simple approach with competitive convergence, diversity, and decision-support capabilities for complex manufacturing optimization problems. Full article

In recent years, as a critical measure of marine fishery resource conservation and ecological restoration in China, stock enhancement has become a hotspot in fishery science research both domestically and internationally. Stock enhancement refers to the replenishment and restoration of biological resources in natural waters through artificial propagation, seed rearing, and releasing, thereby alleviating fishing pressure, improving the ecological environment, and promoting the sustainable utilization of fishery resources. As a major marine fishery nation, China attaches great importance to stock enhancement, utilizing it as an important means to advance the construction of marine ecological civilization, promote green fishery development, and achieve fishery resource recovery. Bibliometric methods allow for the systematic review and visual analysis of massive academic literature, helping to identify research hotspots, reveal knowledge structures, and track disciplinary evolutionary trends. This study systematically investigates literature related to stock enhancement in the China National Knowledge Infrastructure (CNKI) and the Web of Science (WoS) core databases by combining bibliometric analysis, CiteSpace visualization analysis, and Excel statistical analysis. A total of 495 relevant publications were retrieved from CNKI, and 489 from WoS. Concurrently, policy documents from China over the past two decades concerning stock enhancement, aquatic biological resource conservation, and ecological restoration were compiled and analyzed. The results indicate that current research hotspots are primarily concentrated on resource recovery, release effect evaluation, artificial reefs, genetic diversity, ecological restoration, and the collaborative construction of marine ranching. Furthermore, research methodologies have progressively evolved from traditional resource replenishment evaluation toward more refined techniques, such as molecular markers, otolith marking, acoustic telemetry, and ecosystem level assessments. Overall, China’s stock enhancement research is steadily transitioning toward ecological, scientific, precise, and intelligent paradigms, with the long-term monitoring of release effects, collaborative ecosystem restoration, and smart resource management emerging as pivotal future research trends. This study provides a valuable reference for theoretical research, policy formulation, and resource conservation practices in China’s stock enhancement domain. Full article

The Ankyrin-repeat proteins (ANKs) play a key role in plant development and in response to abiotic stress. This research identified family members of the ANK genes in Sorghum bicolor at the whole-genome level, analyzed their sequence characteristics, evolutionary relationships, and expression patterns, and provided a scientific basis for elucidating the functionality of SbANK genes and for salt-tolerant breeding. Using bioinformatics methods, this study conducted a comprehensive identification of the SbANK gene family, analyzing its physicochemical properties, domain composition, chromosomal distribution, colinearity relationships, promoter cis-acting elements, and conserved protein motifs. Transcriptomic data and qRT-PCR were used to detect changes in their expression under salt stress. A total of 186 ANK family members were identified in the Sorghum bicolor genome, classified into 13 subfamilies and unevenly distributed across 10 chromosomes. Intra-species colinearity analysis revealed 7 pairs of duplicated genes, while inter-species colinearity analysis showed that S. bicolor and Oryza sativa share 88 pairs of orthologs, far exceeding the number found in Arabidopsis thaliana (11 pairs). Promoter analysis indicated that SbANK genes are enriched with cis-acting elements associated with hormone responses (particularly MeJA elements, accounting for 51.7%) and stress responses (particularly anaerobic-inducible elements, accounting for 60.9%). Transcriptomic expression analysis revealed that SbANK genes exhibit distinct tissue specificity, with the ANK-IQ subfamily highly expressed in leaves and the ANK-M subfamily showing the most widespread response under salt stress. Expression levels of the 10 candidate genes showing the most significant responses to salt stress were analyzed using qRT-PCR. The results indicated that SbANK91, SbANK135, and SbANK136 were significantly upregulated under 200 mmol/L NaCl treatment. The SbANK family is distinguished by a large number of member genes and structural diversity, with the ANK-M subfamily being the primary group responding to salt stress. SbANK91, SbANK135, and SbANK136 are identified as putative candidate genes for salt stress responses. Full article

As key components of the niche, ecological factors serve as critical parameters for characterizing the dynamic changes in the quality of ecological environments and determining the equilibrium state of ecosystems. Organisms within an ecosystem generally exhibit an intrinsic tendency to evolve toward an optimal niche; this evolutionary process manifests as higher-order nonlinear characteristics and is accompanied by the inherent uncertainty of ecological factor parameters. Interval Type-2 indirect T-S fuzzy control demonstrates excellent robust stability for a class of higher-order nonlinear systems with parameter uncertainty. This paper introduces the niche proximity function into the consequent of the interval Type-2 indirect T-S fuzzy controller to construct a fuzzy control scheme that integrates biological evolutionary characteristics; it further conducts a theoretical analysis of system stability and convergence, deriving adaptive update laws corresponding to ecological factors. Comparative results from simulation examples indicate that interval Type-2 fuzzy control outperforms Type-1 fuzzy control in terms of both system stability and convergence performance. This conclusion not only reflects the adaptive and self-evolving characteristics of biological organisms and their ability to utilize environmental resources but also validates the excellent intelligent control performance of the proposed method. Full article